class_docstr: Linear regression is an approach for modeling a scalar target :math:`y` as
  a linear function of one or more explanatory variables denoted :math:`X`.
  
  Given a set of features :math:`x_i`, and a label :math:`y_i`, linear
  regression interprets the probability that the label is in one class as
  a linear function of a linear combination of the features.
  
      .. math::
        f_i(\theta) =  \theta^T x + \epsilon_i
  
  where :math:`\epsilon_i` is noise.  An intercept term is added by appending
  a column of 1's to the features.  Regularization is often required to
  prevent overfitting by penalizing models with extreme parameter values. The
  linear regression module supports l1 and l2 regularization, which are added
  to the loss function.
  
  The composite objective being optimized for is the following:
  
      .. math::
         \min_{\theta} \sum_{i = 1}^{n} (\theta^Tx - y_i)^2 + \lambda_1 ||\theta||_1 + \lambda_2 ||\theta||^{2}_{2}
  
  where :math:`\lambda_1` is the ``l1_penalty`` and :math:`\lambda_2` is the
  ``l2_penalty``.
  
  This model cannot be constructed directly.  Instead, use
  :func:`turicreate.linear_regression.create` to create an instance
  of this model. A detailed list of parameter options and code samples
  are available in the documentation for the create function.
  
  Examples
  --------
  .. sourcecode:: python
  
      # Load the data (From an S3 bucket)
      >>> data =  turicreate.SFrame('https://static.turi.com/datasets/regression/houses.csv')
  
      # Make a linear regression model
      >>> model = turicreate.linear_regression.create(data, target='price', features=['bath', 'bedroom', 'size'])
  
      # Extract the coefficients
      >>> coefficients = model.coefficients
  
      # Make predictions
      >>> predictions = model.predict(data)
  
      # Evaluate the model
      >>> results = model.evaluate(data)
  
  See Also
  --------
  create
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docstr: Return target value predictions for ``dataset``, using the trained
  linear regression model. This method can be used to get fitted values
  for the model by inputting the training dataset.
  
  Parameters
  ----------
  dataset : SFrame | pandas.Dataframe
      Dataset of new observations. Must include columns with the same
      names as the features used for model training, but does not require
      a target column. Additional columns are ignored.
  
  missing_value_action : str, optional
      Action to perform when missing values are encountered. This can be
      one of:
  
      - 'auto': Default to 'impute'
      - 'impute': Proceed with evaluation by filling in the missing
        values with the mean of the training data. Missing
        values are also imputed if an entire column of data is
        missing during evaluation.
      - 'error': Do not proceed with prediction and terminate with
        an error message.
  
  
  Returns
  -------
  out : SArray
      Predicted target value for each example (i.e. row) in the dataset.
  
  See Also
  ----------
  create, evaluate
  
  Examples
  ----------
  >>> data =  turicreate.SFrame('https://static.turi.com/datasets/regression/houses.csv')
  
  >>> model = turicreate.linear_regression.create(data,
                                       target='price',
                                       features=['bath', 'bedroom', 'size'])
  >>> results = model.predict(data)
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code: def predict(self, dataset, missing_value_action="auto"):
      """
      Return target value predictions for ``dataset``, using the trained
      linear regression model. This method can be used to get fitted values
      for the model by inputting the training dataset.
  
      Parameters
      ----------
      dataset : SFrame | pandas.Dataframe
          Dataset of new observations. Must include columns with the same
          names as the features used for model training, but does not require
          a target column. Additional columns are ignored.
  
      missing_value_action : str, optional
          Action to perform when missing values are encountered. This can be
          one of:
  
          - 'auto': Default to 'impute'
          - 'impute': Proceed with evaluation by filling in the missing
            values with the mean of the training data. Missing
            values are also imputed if an entire column of data is
            missing during evaluation.
          - 'error': Do not proceed with prediction and terminate with
            an error message.
  
  
      Returns
      -------
      out : SArray
          Predicted target value for each example (i.e. row) in the dataset.
  
      See Also
      ----------
      create, evaluate
  
      Examples
      ----------
      >>> data =  turicreate.SFrame('https://static.turi.com/datasets/regression/houses.csv')
  
      >>> model = turicreate.linear_regression.create(data,
                                           target='price',
                                           features=['bath', 'bedroom', 'size'])
      >>> results = model.predict(data)
      """
  
      return super(LinearRegression, self).predict(
          dataset, missing_value_action=missing_value_action
      )
  
file_path: turicreate/src/python/turicreate/toolkits/regression/linear_regression.py
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate.toolkits._supervised_learning
  from turicreate.toolkits._supervised_learning import SupervisedLearningModel
  from turicreate.toolkits._internal_utils import _toolkit_repr_print, _toolkit_get_topk_bottomk, _summarize_coefficients, _raise_error_evaluation_metric_is_valid
label_desc: Use image node_id 8 for calling the LinearRegression obj's underlying member method code with example usage: obj.predict(dataset, missing_value_action) and returns: super